(362c) A Two-State Model-Based Cell Clustering and Network Inference for Single-Cell Gene Expression Data

Authors: 
Papili Gao, N., ETH Zurich
Gunawan, R., ETH Zurich

Recently, the advent of new single-cell
profiling technologies have provided a promising means to elucidate the
mechanisms regulating dynamic cellular processes, including cell development,
maturation, and differentiation (Trapnell, 2015; Paul et
al.
, 2015; Richard et al., 2016). In
particular, conventional techniques for the generation of transcriptomics data
such RNA-sequencing and real-time quantitative PCR have been successfully
adapted for the quantification of gene expression levels in individual cells (Tang
et al., 2009; White et al., 2011).
Single cell analysis allows us to study the significance of cell-to-cell
heterogeneity, which would be impossible using population-averaged measurements
(Sandberg,
2013). Single cell profiling technologies are now able to provide
high-throughput (many cells) high-dimensional (many genes) expression data in a
single experiment, pioneering a whole new series of computational methods for
the data analysis. In this context, the identification and characterization of
different cell types within a heterogeneous population is an important step in
the analysis of single-cell data (Liu and Trapnell, 2016), which
may lead to new insights on the underlying biological process. Standard
unsupervised clustering strategies such as k-means (Kanungo
et al.) and hierarchical clustering (Johnson,
1967) have previously been applied. However, the high variability
in gene expression patterns and the high dimensionality of the data make the
clustering problem not trivial. In recent years, different clustering strategies
have been developed specifically for single-cell expression data by modifying
traditional clustering algorithms and/or by using dimensional reduction
techniques (Lin et al., 2017; Xu and Su, 2015; Buettner et
al.
, 2015; žurauskienė and Yau, 2016; Kiselev et al.,
2017).  In addition, time-variant cell clustering methods
have been implemented to elucidate the appearance of cell types in cell
differentiation stages (Marco et al., 2014; Huang et
al.
, 2014). Until recently (Ezer et al.,
2016), the bursty and stochastic dynamics of gene expression in
single cells, which has been suggested as a major source of the variability (K¾rn
et al., 2005), were not directly addressed in the
clustering of single cell transcriptomics data.

 In this work, we
implemented a clustering algorithm that explicitly takes into account the
stochastic dynamics of gene transcriptional process using the two-state model (Kim
and Marioni, 2013). The two-state model describes the gene
expression processes involving (1) the promoter switching between ON and OFF
state, (2) in the OFF state (a closed chromatin state), the gene is not
accessible for the transcription and (3) in the ON state (an open chromatin
state), the gene transcription could occur, producing the mRNA molecules in
bursts (Munsky
et al., 2012). A total of four kinetic parameters fully
describe the two-state model, namely Kon (rate of activation), Koff (rate of
inactivation), Kt (rate of transcription) and Kd (rate of degradation). Our
clustering approach is based on the idea that cells belonging to the same
cluster should share the same parameters of the two-state model. This concept
has been recently illustrated by Ezer et al. in the implementation of their
single cell clustering method called SABEC (Simulated Annealing for Bursty
Expression Clustering) (Ezer et al., 2016).

Our clustering method starts by
randomly assigning cells into clusters, and iteratively reassigns cells to
clusters until convergence or until a maximum number of iterations, as follow:
(1) for each cluster and each gene, determine the parameters for the two-state
model that best fit the distribution of expression among cells in the cluster,
(2) calculate the probability (likelihood) for each cell to be in each of the
clusters given the cellÕs gene expression data and the cluster parameters from
step (1), and (3) re-assign cells to the cluster that gives the maximum
likelihood for the cellÕs gene expression data. To improve both the accuracy
and stability of the solutions, we performed the iterations above multiple
times with different initial cell assignments, and combine the outcomes in a
consensus matrix that summarize how often two cells are clustered together
(Fig. 1). Finally, we implemented k-medoids (Bhat, 2014) using
the consensus matrix to obtain the cell clustering. The number of clusters can
be user-defined or automatically evaluated based on heuristic approaches such
as gap-statistics or silhouette scores. In comparison to SABEC (Ezer
et al., 2016), the greedy optimization in our algorithm
converged much faster (seconds vs. minutes) while producing comparable
clustering results. For example, in an application to single-cell expression
data from hematopoietic stem cell (HSC) differentiation (Moignard
et al., 2013), each repeat of SABEC took 45 minutes to
complete on a standard workstation, while our greedy algorithm converged to the
final solution in less than one minute.

In the next step, we employed
the cell clustering outcome for two different tasks: (1) to define the trajectory
of gene expression that describes the cell differentiation progression, and (2)
to infer the gene regulatory networks that govern the cell differentiation
process. In these two tasks, we viewed each of the clusters as a ÒstateÓ in the
cell differentiation. In the first task, we first define the cell state of each
cluster based on prior information of either the division stages or the time
stamps of the cells in the cluster. For each cell, we used the two-state model
to compute the cell pseudo-stage or pseudo-time, specifically by taking a
weighted average of the cluster states with the log-likelihood values of the cell
to be in any given cluster as the weights. The cell trajectory was finally constructed
by ordering cells according to their  pseudo-stage or pseudo-time.

In the final task, we employed
the cluster and cell trajectory to portray the cell differentiation
progression, and inferred the gene regulatory network that governs this
process. More specifically, we employed our previous algorithm, SINCERITIES (Papili
Gao et al., 2016), on the ordered cells. Briefly, SINCERITIES
is able to reconstruct the (causal) gene-gene regulations by employing
regularized linear regression, based on changes in the distributions of gene
expressions over the differentiation trajectory. Meanwhile, the signs/modes of
the gene regulations (activation and repression) are inferred by computing
partial correlation analysis between pairs of genes. . We
demonstrated the efficacy of our algorithms for single cell clustering, cell
trajectory construction and gene regulatory network inference on the
differentiation of chicken erythrocytic cells (Richard et al.,
2016). The results further confirmed the importance of sterol
pathways in initiating the cell differentiation process in this particular cell
line.

 

 

 

 

References

 

Bhat,A.
(2014) K-MEDOIDS CLUSTERING USING PARTITIONING AROUND MEDOIDS FOR PERFORMING
FACE RECOGNITION. Int. J. Soft Comput. Math. Control, 3.

Buettner,F. et
al.
(2015) Computational analysis of cell-to-cell heterogeneity in
single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat.
Biotechnol.
, 33, 155–160.

Ezer,D. et
al.
(2016) Determining Physical Mechanisms of Gene Expression Regulation
from Single Cell Gene Expression Data. PLOS Comput. Biol., 12,
e1005072.

Huang,W. et
al.
(2014) Time-variant clustering model for understanding cell fate
decisions. Proc. Natl. Acad. Sci. U. S. A., 111, E4797-806.

Johnson,S.C.
(1967) HIERARCHICAL CLUSTERING SCHEMES. Psychometrika, 32.

K¾rn,M. et
al.
(2005) Stochasticity in gene expression: from theories to phenotypes. Nat.
Rev. Genet.
, 6, 451–464.

Kanungo,T. et
al.
An Efficient k-Means Clustering Algorithm: Analysis and Implementation.

Kim,J. and
Marioni,J.C. (2013) Inferring the kinetics of stochastic gene expression from
single-cell RNA-sequencing data. Genome Biol., 14, R7.

Kiselev,V.Y.
et al. (2017) SC3: consensus clustering of single-cell RNA-seq data. Nat.
Methods
.

Lin,P. et
al.
(2017) CIDR: Ultrafast and accurate clustering through imputation for
single-cell RNA-seq data. Genome Biol., 18, 59.

Liu,S. and
Trapnell,C. (2016) Single-cell transcriptome sequencing: recent advances and
remaining challenges. F1000Research, 5.

Marco,E. et
al.
(2014) Bifurcation analysis of single-cell gene expression data reveals
epigenetic landscape. Proc. Natl. Acad. Sci. U. S. A., 111,
5643–50.

Moignard,V. et
al.
(2013) Characterization of transcriptional networks in blood stem and
progenitor cells using high-throughput single-cell gene expression analysis. Nat.
Cell Biol.
, 15, 363–72.

Munsky,B. et
al.
(2012) Using Gene Expression Noise to Understand Gene Regulation. Science
(80-. ).
, 336.

Papili
Gao,N. et al. (2016) SINCERITIES: Inferring gene regulatory networks
from time-stamped single cell transcriptional expression profiles. bioRxiv.

Paul,F. et
al.
(2015) Transcriptional Heterogeneity and Lineage Commitment in Myeloid
Progenitors. Cell, 163, 1663–1677.

Richard,A. et
al.
(2016) Single-Cell-Based Analysis Highlights a Surge in Cell-to-Cell
Molecular Variability Preceding Irreversible Commitment in a Differentiation
Process. PLOS Biol., 14, e1002585.

Sandberg,R.
(2013) Entering the era of single-cell transcriptomics in biology and medicine.
Nat. Methods, 11, 22–24.

Tang,F. et
al.
(2009) mRNA-Seq whole-transcriptome analysis of a single cell. Nat.
Methods
, 6, 377–382.

Trapnell,C.
(2015) Defining cell types and states with single-cell genomics. Genome Res.,
25, 1491–8.

White,A.K. et
al.
(2011) High-throughput microfluidic single-cell RT-qPCR. Proc. Natl.
Acad. Sci. U. S. A.
, 108, 13999–4004.

Xu,C. and
Su,Z. (2015) Identification of cell types from single-cell transcriptomes using
a novel clustering method. Bioinformatics, 31, 1974–80.

žurauskienė,J.
and Yau,C. (2016) pcaReduce: hierarchical clustering of single cell
transcriptional profiles. BMC Bioinformatics, 17, 140.

 

 

Figure 1: Cell clustering in HSC differentiation
using single cell transcriptional expression data. (a) The single cell
expression data came from five populations of cells during hematopoiesis: LMPP
(lymphoid-primed multipotential progenitor), PreM (premegakaryocytes), GMP
(granulocyte-macrophage progenitor) and CLP (common lymphoid progenitors). (b)
Maximum-likelihood based clustering of cells with our greedy algorithm.